CoinShares 挖矿报告：减半及其对算力和矿工成本结构的影响

The growth and sustainability of the bitcoin mining network in has aroused people's doubts about its environmental sustainability and profitability, especially the efficiency and energy cost of the network. The difficulty adjustment mechanism of bitcoin mining ensures the inflexibility of supply. After halving, miners with higher costs may be in trouble due to the reduction of direct income. This paper evaluates the average production cost of each bitcoin after halving. The results show that the average cost is USD, and the efficiency of the mining network is improved. Although the power demand of the network is increasing, the efficiency has been significantly improved. Using random number data is helpful to analyze the efficiency of different mining models. At present, the average efficiency of the network is expected to drop to bitcoin mining and environmental impact by the middle of the year. Bitcoin mining usually uses stagnant energy, which means that about bitcoin mining energy is a sustainable source, and bitcoin mining can significantly reduce nature. The emission caused by gas combustion is a major environmental problem. The financial analysis of miners will change after halving the annual production cost and profit structure of miners. Our analysis focuses on the different cost structures of listed miners and their vulnerability to halving, and the conclusion and positioning of miners. Most miners will face high-cost challenges and need to reduce costs to maintain profitability. If the price of bitcoin remains above US dollars, it is expected that only a few miners will be able to make profits. Bitcoin mining network will grow. The network has experienced rapid growth to the annual computing power. This rapid expansion has aroused people's concern about its sustainability, whether from the environmental point of view or from the profitability of the mining network. In this document, we aim to solve these two problems. The following is the final result of the average production cost of each miner after each bitcoin is halved, which highlights that the average production cost is US dollars. Although bitcoin mining has some similarities with traditional mining, it consumes energy to generate valuable resources. The similarity between production and production is basically limited to the unique self-adjustment mechanism called difficulty adjustment in bitcoin mining, which can ensure that the supply remains strictly inelastic. At some point in the bitcoin mining cycle, miners at the high end of the cost curve will begin to suffer losses and the hash rate will begin to decline because the price increase is not enough to offset the increase in mining difficulty, regardless of the number of miners or the number of newly created bitcoins will remain unchanged until the next scheduled halving event. If the network is collective, The mining difficulty will be adjusted upwards to maintain the target productivity, so that the miners with higher costs will be squeezed out of the market. Our analysis focuses on the different cost structures of listed miners and the miners who are most vulnerable to halving the annual rate. In order to predict the future direction of the mining power, our best method is to analyze the historical model. Qualitative reasoning shows that the growth will be driven in part by the price of bitcoin. The positive growth prospect may encourage miners to improve their mining power and think it is profitable, but it depends on the future. The review of historical data under the assumption of price shows that mining activities have increased between the halving events. However, it is challenging to determine a clear cycle because of its exponential growth. We have done some work in this regard because the computing power is often an unstable number. Using the deviation of historical data trends will produce more accurate results than purely qualitative methods. However, a key problem is that most trend lines contain future information, which means that the trend lines we see today are in the past. It will be different, so the most reliable method is to calculate a trend line based on the out-of-sample data, which is not affected by the future development. The data reveals an interesting regular pattern between halving cycles, indicating that the current peak of computing power is not uncommon from the historical point of view of this point in the cycle. The following figure illustrates this trend more clearly. The chart clearly shows the periodicity of these changes. Since the first bitcoin halving in and the subsequent halving in, a pattern has emerged, namely After halving, the computing power usually falls below the trend line. This situation usually lasts for about six months or so, which is somewhat special. Because the mining ban in China has been greatly extended, the price has fallen below the trend line. However, this model usually involves the initial decline of computing power, then the recovery in the middle of the cycle, and then there is a sharp increase in activity about one year before the next halving. This cycle is logical. In order to remain competitive in the expectation of halving, miners have increased their capital expenditure to make the computing power significantly higher than the trend after halving. The decrease of miners' direct income has affected their capital expenditure cycle, and the current cycle is no exception. It is worth noting that the peak of computing power growth usually appears about four months before halving. This may be because the bitcoin boom has caused the mining difficulty to soar, thus forcing miners and mining machines with higher production costs to withdraw from the current mining difficulty, which is at a historical high level and consistent with the relative peak observed in the previous cycle. How will the future of bitcoin computing power be guided by historical trends? We may predict that by. When the month is halved, the computing power will return to the trend line of about every second. After six months, it may further decline. After that, the trend line predicts that the computing power will increase sharply to about the end of the year. This halving may eliminate those at the high end of the cost curve and give those with sufficient liquidity the opportunity to buy hardware at a discounted price. This situation depends to a large extent on whether the price rises above the average production cost of each miner and may require a sharp drop in prices or transaction costs. For example, the decline of ordinal number usage improves the efficiency of mining network. At present, the mining equipment used in Bitcoin is diverse, covering a series of power consumption levels and the resulting efficiency. Historically, this diversity has made it challenging to determine the overall efficiency of mining teams. Some noteworthy studies have been made on hardware fingerprint identification using random data. In order to avoid studying the technical details too deeply, we found that each miner model will leave obvious steam on the bitcoin blockchain. The trace can then be analyzed to determine the distribution of different mining models in the network. Because the efficiency of each mining model is in Watt Terahi, the overall efficiency of the entire bitcoin mining queue can be calculated. In view of the fairly linear progress of this path, the future trend can also be predicted. At present, the weighted average efficiency of the network is only this year, and the efficiency has improved in the past three years. According to these trends, it is expected that by the middle of the year, with the continuous improvement of chip design and the online efficiency of more efficient mining hardware, the level of bitcoin mining will be as low as the cheapest energy. 比特币今日价格行情网_okx交易所app_永续合约_比特币怎么买卖交易_虚拟币交易所平台

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